{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas Operations\n",
    "\n",
    "### Dr. Tirthajyoti Sarkar, Fremont, CA, 94536.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Series\n",
    "### Loading packages and initializations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Labels: ['a', 'b', 'c']\n",
      "My data: [10, 20, 30]\n",
      "Dictionary: {'a': 10, 'b': 20, 'c': 30}\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "labels = ['a','b','c']\n",
    "my_data = [10,20,30]\n",
    "arr = np.array(my_data)\n",
    "d = {'a':10,'b':20,'c':30}\n",
    "\n",
    "print (\"Labels:\", labels)\n",
    "print(\"My data:\", my_data)\n",
    "print(\"Dictionary:\", d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a Series (Pandas class)\n",
    "* From numerical data only\n",
    "* From numerical data and corresponding index (row labels)\n",
    "* From NumPy array as the source of numerical data\n",
    "* Just using a pre-defined dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10\n",
       "1    20\n",
       "2    30\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(data=my_data) # Output looks very similar to a NumPy array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    10\n",
       "b    20\n",
       "c    30\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(data=my_data, index=labels) # Note the extra information about index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    10\n",
       "b    20\n",
       "c    30\n",
       "dtype: int32"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inputs are in order of the expected parameters (not explicitly named), NumPy array is used for data\n",
    "pd.Series(arr, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    10\n",
       "b    20\n",
       "c    30\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(d) # Using a pre-defined Dictionary object"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What type of values can a Pandas Series hold?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Holding numerical data\n",
      "-------------------------\n",
      "0    10\n",
      "1    20\n",
      "2    30\n",
      "dtype: int32\n",
      "\n",
      "Holding text labels\n",
      "--------------------\n",
      "0    a\n",
      "1    b\n",
      "2    c\n",
      "dtype: object\n",
      "\n",
      "Holding functions\n",
      "--------------------\n",
      "0      <built-in function sum>\n",
      "1    <built-in function print>\n",
      "2      <built-in function len>\n",
      "dtype: object\n",
      "\n",
      "Holding objects from a dictionary\n",
      "----------------------------------------\n",
      "0    <built-in method keys of dict object at 0x0000...\n",
      "1    <built-in method items of dict object at 0x000...\n",
      "2    <built-in method values of dict object at 0x00...\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print (\"\\nHolding numerical data\\n\",'-'*25, sep='')\n",
    "print(pd.Series(arr))\n",
    "print (\"\\nHolding text labels\\n\",'-'*20, sep='')\n",
    "print(pd.Series(labels))\n",
    "print (\"\\nHolding functions\\n\",'-'*20, sep='')\n",
    "print(pd.Series(data=[sum,print,len]))\n",
    "print (\"\\nHolding objects from a dictionary\\n\",'-'*40, sep='')\n",
    "print(pd.Series(data=[d.keys, d.items, d.values]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Indexing and slicing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Indexing by name of the item/object (string identifier)\n",
      "--------------------------------------------------------\n",
      "Value for CA in ser1: 1\n",
      "Value for AZ in ser1: 4\n",
      "Value for NV in ser2: 5\n",
      "\n",
      "Indexing by number (positional value in the series)\n",
      "----------------------------------------------------\n",
      "Value for CA in ser1: 1\n",
      "Value for AZ in ser1: 4\n",
      "Value for NV in ser2: 5\n",
      "\n",
      "Indexing by a range\n",
      "-------------------------\n",
      "Value for OR, CO, and AZ in ser1:\n",
      "OR    2\n",
      "CO    3\n",
      "AZ    4\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "ser1 = pd.Series([1,2,3,4],['CA', 'OR', 'CO', 'AZ'])\n",
    "ser2 = pd.Series([1,2,5,4],['CA', 'OR', 'NV', 'AZ'])\n",
    "\n",
    "print (\"\\nIndexing by name of the item/object (string identifier)\\n\",'-'*56, sep='')\n",
    "print(\"Value for CA in ser1:\", ser1['CA'])\n",
    "print(\"Value for AZ in ser1:\", ser1['AZ'])\n",
    "print(\"Value for NV in ser2:\", ser2['NV'])\n",
    "\n",
    "print (\"\\nIndexing by number (positional value in the series)\\n\",'-'*52, sep='')\n",
    "print(\"Value for CA in ser1:\", ser1[0])\n",
    "print(\"Value for AZ in ser1:\", ser1[3])\n",
    "print(\"Value for NV in ser2:\", ser2[2])\n",
    "\n",
    "print (\"\\nIndexing by a range\\n\",'-'*25, sep='')\n",
    "print (\"Value for OR, CO, and AZ in ser1:\\n\", ser1[1:4], sep='')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding/Merging two series with common indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "After adding the two series, the result looks like this...\n",
      "-----------------------------------------------------------\n",
      "AZ    8.0\n",
      "CA    2.0\n",
      "CO    NaN\n",
      "NV    NaN\n",
      "OR    4.0\n",
      "dtype: float64\n",
      "\n",
      "Python tries to add values where it finds common index name, and puts NaN where indices are missing\n",
      "\n",
      "\n",
      "The idea works even for multiplication...\n",
      "-------------------------------------------\n",
      "AZ    16.0\n",
      "CA     1.0\n",
      "CO     NaN\n",
      "NV     NaN\n",
      "OR     4.0\n",
      "dtype: float64\n",
      "\n",
      "Or even for combination of mathematical operations!\n",
      "-----------------------------------------------------\n",
      "AZ    55.200210\n",
      "CA     2.718282\n",
      "CO          NaN\n",
      "NV          NaN\n",
      "OR     7.690086\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "ser1 = pd.Series([1,2,3,4],['CA', 'OR', 'CO', 'AZ'])\n",
    "ser2 = pd.Series([1,2,5,4],['CA', 'OR', 'NV', 'AZ'])\n",
    "ser3 = ser1+ser2\n",
    "\n",
    "print (\"\\nAfter adding the two series, the result looks like this...\\n\",'-'*59, sep='')\n",
    "print(ser3)\n",
    "print(\"\\nPython tries to add values where it finds common index name, and puts NaN where indices are missing\\n\")\n",
    "\n",
    "print (\"\\nThe idea works even for multiplication...\\n\",'-'*43, sep='')\n",
    "print (ser1*ser2)\n",
    "\n",
    "print (\"\\nOr even for combination of mathematical operations!\\n\",'-'*53, sep='')\n",
    "print (np.exp(ser1)+np.log10(ser2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DataFrame (the Real Meat!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "from numpy.random import randn as rn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating and accessing DataFrame\n",
    "* Indexing\n",
    "* Adding and deleting rows and columns\n",
    "* Subsetting DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The data frame looks like\n",
      "---------------------------------------------\n",
      "          W         X         Y         Z\n",
      "A  2.706850  0.628133  0.907969  0.503826\n",
      "B  0.651118 -0.319318 -0.848077  0.605965\n",
      "C -2.018168  0.740122  0.528813 -0.589001\n",
      "D  0.188695 -0.758872 -0.933237  0.955057\n",
      "E  0.190794  1.978757  2.605967  0.683509\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(101)\n",
    "matrix_data = rn(5,4)\n",
    "row_labels = ['A','B','C','D','E']\n",
    "column_headings = ['W','X','Y','Z']\n",
    "\n",
    "df = pd.DataFrame(data=matrix_data, index=row_labels, columns=column_headings)\n",
    "print(\"\\nThe data frame looks like\\n\",'-'*45, sep='')\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Indexing and slicing (columns)\n",
    "* By bracket method\n",
    "* By DOT method (NOT recommended)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The 'X' column\n",
      "-------------------------\n",
      "A    0.628133\n",
      "B   -0.319318\n",
      "C    0.740122\n",
      "D   -0.758872\n",
      "E    1.978757\n",
      "Name: X, dtype: float64\n",
      "\n",
      "Type of the column: <class 'pandas.core.series.Series'>\n",
      "\n",
      "The 'X' and 'Z' columns indexed by passing a list\n",
      "-------------------------------------------------------\n",
      "          X         Z\n",
      "A  0.628133  0.503826\n",
      "B -0.319318  0.605965\n",
      "C  0.740122 -0.589001\n",
      "D -0.758872  0.955057\n",
      "E  1.978757  0.683509\n",
      "\n",
      "Type of the pair of columns: <class 'pandas.core.frame.DataFrame'>\n",
      "\n",
      "So, for more than one column, the object turns into a DataFrame\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nThe 'X' column\\n\",'-'*25, sep='')\n",
    "print(df['X'])\n",
    "print(\"\\nType of the column: \", type(df['X']), sep='')\n",
    "print(\"\\nThe 'X' and 'Z' columns indexed by passing a list\\n\",'-'*55, sep='')\n",
    "print(df[['X','Z']])\n",
    "print(\"\\nType of the pair of columns: \", type(df[['X','Z']]), sep='')\n",
    "print (\"\\nSo, for more than one column, the object turns into a DataFrame\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The 'X' column accessed by DOT method (NOT recommended)\n",
      "-------------------------------------------------------\n",
      "A    0.628133\n",
      "B   -0.319318\n",
      "C    0.740122\n",
      "D   -0.758872\n",
      "E    1.978757\n",
      "Name: X, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nThe 'X' column accessed by DOT method (NOT recommended)\\n\",'-'*55, sep='')\n",
    "print(df.X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating and deleting a (new) column (or row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "A column is created by assigning it in relation to an existing column\n",
      "---------------------------------------------------------------------------\n",
      "          W         X         Y         Z       New  New (Sum of X and Z)\n",
      "A  2.706850  0.628133  0.907969  0.503826  1.131958              1.131958\n",
      "B  0.651118 -0.319318 -0.848077  0.605965  0.286647              0.286647\n",
      "C -2.018168  0.740122  0.528813 -0.589001  0.151122              0.151122\n",
      "D  0.188695 -0.758872 -0.933237  0.955057  0.196184              0.196184\n",
      "E  0.190794  1.978757  2.605967  0.683509  2.662266              2.662266\n",
      "\n",
      "A column is dropped by using df.drop() method\n",
      "-------------------------------------------------------\n",
      "          W         X         Y         Z  New (Sum of X and Z)\n",
      "A  2.706850  0.628133  0.907969  0.503826              1.131958\n",
      "B  0.651118 -0.319318 -0.848077  0.605965              0.286647\n",
      "C -2.018168  0.740122  0.528813 -0.589001              0.151122\n",
      "D  0.188695 -0.758872 -0.933237  0.955057              0.196184\n",
      "E  0.190794  1.978757  2.605967  0.683509              2.662266\n",
      "\n",
      "A row (index) is dropped by using df.drop() method and axis=0\n",
      "-----------------------------------------------------------------\n",
      "          W         X         Y         Z  New (Sum of X and Z)\n",
      "B  0.651118 -0.319318 -0.848077  0.605965              0.286647\n",
      "C -2.018168  0.740122  0.528813 -0.589001              0.151122\n",
      "D  0.188695 -0.758872 -0.933237  0.955057              0.196184\n",
      "E  0.190794  1.978757  2.605967  0.683509              2.662266\n",
      "\n",
      "An in-place change can be done by making inplace=True in the drop method\n",
      "---------------------------------------------------------------------------\n",
      "          W         X         Y         Z\n",
      "A  2.706850  0.628133  0.907969  0.503826\n",
      "B  0.651118 -0.319318 -0.848077  0.605965\n",
      "C -2.018168  0.740122  0.528813 -0.589001\n",
      "D  0.188695 -0.758872 -0.933237  0.955057\n",
      "E  0.190794  1.978757  2.605967  0.683509\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nA column is created by assigning it in relation to an existing column\\n\",'-'*75, sep='')\n",
    "df['New'] = df['X']+df['Z']\n",
    "df['New (Sum of X and Z)'] = df['X']+df['Z']\n",
    "print(df)\n",
    "print(\"\\nA column is dropped by using df.drop() method\\n\",'-'*55, sep='')\n",
    "df = df.drop('New', axis=1) # Notice the axis=1 option, axis = 0 is default, so one has to change it to 1\n",
    "print(df)\n",
    "df1=df.drop('A')\n",
    "print(\"\\nA row (index) is dropped by using df.drop() method and axis=0\\n\",'-'*65, sep='')\n",
    "print(df1)\n",
    "print(\"\\nAn in-place change can be done by making inplace=True in the drop method\\n\",'-'*75, sep='')\n",
    "df.drop('New (Sum of X and Z)', axis=1, inplace=True)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selecting/indexing Rows\n",
    "* Label-based 'loc' method\n",
    "* Index (numeric) 'iloc' method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Label-based 'loc' method can be used for selecting row(s)\n",
      "------------------------------------------------------------\n",
      "\n",
      "Single row\n",
      "\n",
      "W   -2.018168\n",
      "X    0.740122\n",
      "Y    0.528813\n",
      "Z   -0.589001\n",
      "Name: C, dtype: float64\n",
      "\n",
      "Multiple rows\n",
      "\n",
      "          W         X         Y         Z\n",
      "B  0.651118 -0.319318 -0.848077  0.605965\n",
      "C -2.018168  0.740122  0.528813 -0.589001\n",
      "\n",
      "Index position based 'iloc' method can be used for selecting row(s)\n",
      "----------------------------------------------------------------------\n",
      "\n",
      "Single row\n",
      "\n",
      "W   -2.018168\n",
      "X    0.740122\n",
      "Y    0.528813\n",
      "Z   -0.589001\n",
      "Name: C, dtype: float64\n",
      "\n",
      "Multiple rows\n",
      "\n",
      "          W         X         Y         Z\n",
      "B  0.651118 -0.319318 -0.848077  0.605965\n",
      "C -2.018168  0.740122  0.528813 -0.589001\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nLabel-based 'loc' method can be used for selecting row(s)\\n\",'-'*60, sep='')\n",
    "print(\"\\nSingle row\\n\")\n",
    "print(df.loc['C'])\n",
    "print(\"\\nMultiple rows\\n\")\n",
    "print(df.loc[['B','C']])\n",
    "print(\"\\nIndex position based 'iloc' method can be used for selecting row(s)\\n\",'-'*70, sep='')\n",
    "print(\"\\nSingle row\\n\")\n",
    "print(df.iloc[2])\n",
    "print(\"\\nMultiple rows\\n\")\n",
    "print(df.iloc[[1,2]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Subsetting DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The DatFrame\n",
      "---------------------------------------------\n",
      "          W         X         Y         Z\n",
      "A  2.706850  0.628133  0.907969  0.503826\n",
      "B  0.651118 -0.319318 -0.848077  0.605965\n",
      "C -2.018168  0.740122  0.528813 -0.589001\n",
      "D  0.188695 -0.758872 -0.933237  0.955057\n",
      "E  0.190794  1.978757  2.605967  0.683509\n",
      "\n",
      "Element at row 'B' and column 'Y' is\n",
      "\n",
      "-0.848076983404\n",
      "\n",
      "Subset comprising of rows B and D, and columns W and Y, is\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>W</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.651118</td>\n",
       "      <td>-0.848077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.188695</td>\n",
       "      <td>-0.933237</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W         Y\n",
       "B  0.651118 -0.848077\n",
       "D  0.188695 -0.933237"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(101)\n",
    "matrix_data = rn(5,4)\n",
    "row_labels = ['A','B','C','D','E']\n",
    "column_headings = ['W','X','Y','Z']\n",
    "df = pd.DataFrame(data=matrix_data, index=row_labels, columns=column_headings)\n",
    "\n",
    "print(\"\\nThe DatFrame\\n\",'-'*45, sep='')\n",
    "print(df)\n",
    "print(\"\\nElement at row 'B' and column 'Y' is\\n\") \n",
    "print(df.loc['B','Y'])\n",
    "print(\"\\nSubset comprising of rows B and D, and columns W and Y, is\\n\")\n",
    "df.loc[['B','D'],['W','Y']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conditional selection, index (re)setting, multi-index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basic idea of conditional check and Boolean DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The DataFrame\n",
      "---------------------------------------------\n",
      "          W         X         Y         Z\n",
      "A  2.706850  0.628133  0.907969  0.503826\n",
      "B  0.651118 -0.319318 -0.848077  0.605965\n",
      "C -2.018168  0.740122  0.528813 -0.589001\n",
      "D  0.188695 -0.758872 -0.933237  0.955057\n",
      "E  0.190794  1.978757  2.605967  0.683509\n",
      "\n",
      "Boolean DataFrame(s) where we are checking if the values are greater than 0\n",
      "---------------------------------------------------------------------------\n",
      "       W      X      Y      Z\n",
      "A   True   True   True   True\n",
      "B   True  False  False   True\n",
      "C  False   True   True  False\n",
      "D   True  False  False   True\n",
      "E   True   True   True   True\n",
      "\n",
      "\n",
      "       W      X      Y      Z\n",
      "A   True   True   True   True\n",
      "B   True  False  False   True\n",
      "C  False   True   True  False\n",
      "\n",
      "DataFrame indexed by boolean dataframe\n",
      "---------------------------------------------\n",
      "          W         X         Y         Z\n",
      "A  2.706850  0.628133  0.907969  0.503826\n",
      "B  0.651118       NaN       NaN  0.605965\n",
      "C       NaN  0.740122  0.528813       NaN\n",
      "D  0.188695       NaN       NaN  0.955057\n",
      "E  0.190794  1.978757  2.605967  0.683509\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nThe DataFrame\\n\",'-'*45, sep='')\n",
    "print(df)\n",
    "print(\"\\nBoolean DataFrame(s) where we are checking if the values are greater than 0\\n\",'-'*75, sep='')\n",
    "print(df>0)\n",
    "print(\"\\n\")\n",
    "print(df.loc[['A','B','C']]>0)\n",
    "booldf = df>0\n",
    "print(\"\\nDataFrame indexed by boolean dataframe\\n\",'-'*45, sep='')\n",
    "print(df[booldf])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Passing Boolean series to conditionally subset the DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "A new DataFrame\n",
      "-------------------------\n",
      "   Age  Height  Weight\n",
      "A   22      66     140\n",
      "B   42      70     148\n",
      "C   30      62     125\n",
      "D   35      68     160\n",
      "E   25      62     152\n",
      "\n",
      "Rows with Height > 65 inch\n",
      "-----------------------------------\n",
      "   Age  Height  Weight\n",
      "A   22      66     140\n",
      "B   42      70     148\n",
      "D   35      68     160\n",
      "\n",
      "Rows with Height > 65 inch and Weight >145 lbs\n",
      "-------------------------------------------------------\n",
      "   Age  Height  Weight\n",
      "B   42      70     148\n",
      "D   35      68     160\n",
      "\n",
      "DataFrame with only Age and Weight columns whose Height > 65 inch\n",
      "--------------------------------------------------------------------\n",
      "   Age  Weight\n",
      "A   22     140\n",
      "B   42     148\n",
      "D   35     160\n"
     ]
    }
   ],
   "source": [
    "matrix_data = np.matrix('22,66,140;42,70,148;30,62,125;35,68,160;25,62,152')\n",
    "row_labels = ['A','B','C','D','E']\n",
    "column_headings = ['Age', 'Height', 'Weight']\n",
    "\n",
    "df = pd.DataFrame(data=matrix_data, index=row_labels, columns=column_headings)\n",
    "print(\"\\nA new DataFrame\\n\",'-'*25, sep='')\n",
    "print(df)\n",
    "print(\"\\nRows with Height > 65 inch\\n\",'-'*35, sep='')\n",
    "print(df[df['Height']>65])\n",
    "\n",
    "booldf1 = df['Height']>65\n",
    "booldf2 = df['Weight']>145\n",
    "print(\"\\nRows with Height > 65 inch and Weight >145 lbs\\n\",'-'*55, sep='')\n",
    "print(df[(booldf1) & (booldf2)])\n",
    "\n",
    "print(\"\\nDataFrame with only Age and Weight columns whose Height > 65 inch\\n\",'-'*68, sep='')\n",
    "print(df[booldf1][['Age','Weight']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Re-setting and Setting Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The DataFrame\n",
      "-------------------------\n",
      "   Age  Height  Weight\n",
      "A   22      66     140\n",
      "B   42      70     148\n",
      "C   30      62     125\n",
      "D   35      68     160\n",
      "E   25      62     152\n",
      "\n",
      "After resetting index\n",
      "-----------------------------------\n",
      "  index  Age  Height  Weight\n",
      "0     A   22      66     140\n",
      "1     B   42      70     148\n",
      "2     C   30      62     125\n",
      "3     D   35      68     160\n",
      "4     E   25      62     152\n",
      "\n",
      "After resetting index with 'drop' option TRUE\n",
      "---------------------------------------------\n",
      "   Age  Height  Weight\n",
      "0   22      66     140\n",
      "1   42      70     148\n",
      "2   30      62     125\n",
      "3   35      68     160\n",
      "4   25      62     152\n",
      "\n",
      "Adding a new column 'Profession'\n",
      "---------------------------------------------\n",
      "   Age  Height  Weight Profession\n",
      "A   22      66     140    Student\n",
      "B   42      70     148    Teacher\n",
      "C   30      62     125   Engineer\n",
      "D   35      68     160     Doctor\n",
      "E   25      62     152      Nurse\n",
      "\n",
      "Setting 'Profession' column as index\n",
      "---------------------------------------------\n",
      "            Age  Height  Weight\n",
      "Profession                     \n",
      "Student      22      66     140\n",
      "Teacher      42      70     148\n",
      "Engineer     30      62     125\n",
      "Doctor       35      68     160\n",
      "Nurse        25      62     152\n"
     ]
    }
   ],
   "source": [
    "matrix_data = np.matrix('22,66,140;42,70,148;30,62,125;35,68,160;25,62,152')\n",
    "row_labels = ['A','B','C','D','E']\n",
    "column_headings = ['Age', 'Height', 'Weight']\n",
    "\n",
    "df = pd.DataFrame(data=matrix_data, index=row_labels, columns=column_headings)\n",
    "print(\"\\nThe DataFrame\\n\",'-'*25, sep='')\n",
    "print(df)\n",
    "print(\"\\nAfter resetting index\\n\",'-'*35, sep='')\n",
    "print(df.reset_index())\n",
    "print(\"\\nAfter resetting index with 'drop' option TRUE\\n\",'-'*45, sep='')\n",
    "print(df.reset_index(drop=True))\n",
    "print(\"\\nAdding a new column 'Profession'\\n\",'-'*45, sep='')\n",
    "df['Profession'] = \"Student Teacher Engineer Doctor Nurse\".split()\n",
    "print(df)\n",
    "print(\"\\nSetting 'Profession' column as index\\n\",'-'*45, sep='')\n",
    "print (df.set_index('Profession'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multi-indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Tuple pairs after the zip and list command\n",
      "---------------------------------------------\n",
      "[('G1', 1), ('G1', 2), ('G1', 3), ('G2', 1), ('G2', 2), ('G2', 3)]\n",
      "\n",
      "Index hierarchy\n",
      "-------------------------\n",
      "MultiIndex(levels=[['G1', 'G2'], [1, 2, 3]],\n",
      "           labels=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]])\n",
      "\n",
      "Index hierarchy type\n",
      "-------------------------\n",
      "<class 'pandas.core.indexes.multi.MultiIndex'>\n",
      "\n",
      "Creating DataFrame with multi-index\n",
      "-------------------------------------\n",
      "         A     B     C\n",
      "G1 1  2.71  0.63  0.91\n",
      "   2  0.50  0.65 -0.32\n",
      "   3 -0.85  0.61 -2.02\n",
      "G2 1  0.74  0.53 -0.59\n",
      "   2  0.19 -0.76 -0.93\n",
      "   3  0.96  0.19  1.98\n",
      "\n",
      "Subsetting multi-index DataFrame using two 'loc' methods\n",
      "------------------------------------------------------------\n",
      "      B     C\n",
      "1  0.53 -0.59\n",
      "3  0.19  1.98\n",
      "\n",
      "Naming the indices by 'index.names' method\n",
      "---------------------------------------------\n",
      "                A     B     C\n",
      "Outer Inner                  \n",
      "G1    1      2.71  0.63  0.91\n",
      "      2      0.50  0.65 -0.32\n",
      "      3     -0.85  0.61 -2.02\n",
      "G2    1      0.74  0.53 -0.59\n",
      "      2      0.19 -0.76 -0.93\n",
      "      3      0.96  0.19  1.98\n"
     ]
    }
   ],
   "source": [
    "# Index Levels\n",
    "outside = ['G1','G1','G1','G2','G2','G2']\n",
    "inside = [1,2,3,1,2,3]\n",
    "hier_index = list(zip(outside,inside))\n",
    "\n",
    "print(\"\\nTuple pairs after the zip and list command\\n\",'-'*45, sep='')\n",
    "print(hier_index)\n",
    "hier_index = pd.MultiIndex.from_tuples(hier_index)\n",
    "print(\"\\nIndex hierarchy\\n\",'-'*25, sep='')\n",
    "print(hier_index)\n",
    "print(\"\\nIndex hierarchy type\\n\",'-'*25, sep='')\n",
    "print(type(hier_index))\n",
    "\n",
    "print(\"\\nCreating DataFrame with multi-index\\n\",'-'*37, sep='')\n",
    "np.random.seed(101)\n",
    "df1 = pd.DataFrame(data=np.round(rn(6,3),2), index= hier_index, columns= ['A','B','C'])\n",
    "print(df1)\n",
    "\n",
    "print(\"\\nSubsetting multi-index DataFrame using two 'loc' methods\\n\",'-'*60, sep='')\n",
    "print(df1.loc['G2'].loc[[1,3]][['B','C']])\n",
    "\n",
    "print(\"\\nNaming the indices by 'index.names' method\\n\",'-'*45, sep='')\n",
    "df1.index.names=['Outer', 'Inner']\n",
    "print(df1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross-section ('XS') command"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Grabbing a cross-section from outer level\n",
      "---------------------------------------------\n",
      "          A     B     C\n",
      "Inner                  \n",
      "1      2.71  0.63  0.91\n",
      "2      0.50  0.65 -0.32\n",
      "3     -0.85  0.61 -2.02\n",
      "\n",
      "Grabbing a cross-section from inner level (for all outer levels)\n",
      "-----------------------------------------------------------------\n",
      "          A     B     C\n",
      "Outer                  \n",
      "G1     0.50  0.65 -0.32\n",
      "G2     0.19 -0.76 -0.93\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nGrabbing a cross-section from outer level\\n\",'-'*45, sep='')\n",
    "print(df1.xs('G1'))\n",
    "print(\"\\nGrabbing a cross-section from inner level (for all outer levels)\\n\",'-'*65, sep='')\n",
    "print(df1.xs(2,level='Inner'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          A    B  C\n",
      "States             \n",
      "CA      1.0  5.0  1\n",
      "NV      2.0  NaN  2\n",
      "AZ      NaN  NaN  3\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame({'A':[1,2,np.nan],'B':[5,np.nan,np.nan],'C':[1,2,3]})\n",
    "df['States']=\"CA NV AZ\".split()\n",
    "df.set_index('States',inplace=True)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas 'dropna' method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Dropping any rows with a NaN value\n",
      "-----------------------------------\n",
      "          A    B  C\n",
      "States             \n",
      "CA      1.0  5.0  1\n",
      "\n",
      "Dropping any column with a NaN value\n",
      "-----------------------------------\n",
      "        C\n",
      "States   \n",
      "CA      1\n",
      "NV      2\n",
      "AZ      3\n",
      "\n",
      "Dropping a row with a minimum 2 NaN value using 'thresh' parameter\n",
      "--------------------------------------------------------------------\n",
      "          A    B  C\n",
      "States             \n",
      "CA      1.0  5.0  1\n",
      "NV      2.0  NaN  2\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nDropping any rows with a NaN value\\n\",'-'*35, sep='')\n",
    "print(df.dropna(axis=0))\n",
    "print(\"\\nDropping any column with a NaN value\\n\",'-'*35, sep='')\n",
    "print(df.dropna(axis=1))\n",
    "print(\"\\nDropping a row with a minimum 2 NaN value using 'thresh' parameter\\n\",'-'*68, sep='')\n",
    "print(df.dropna(axis=0, thresh=2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas 'fillna' method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Filling values with a default value\n",
      "-----------------------------------\n",
      "                 A           B  C\n",
      "States                           \n",
      "CA               1           5  1\n",
      "NV               2  FILL VALUE  2\n",
      "AZ      FILL VALUE  FILL VALUE  3\n",
      "\n",
      "Filling values with a computed value (mean of column A here)\n",
      "------------------------------------------------------------\n",
      "          A    B  C\n",
      "States             \n",
      "CA      1.0  5.0  1\n",
      "NV      2.0  1.5  2\n",
      "AZ      1.5  1.5  3\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nFilling values with a default value\\n\",'-'*35, sep='')\n",
    "print(df.fillna(value='FILL VALUE'))\n",
    "print(\"\\nFilling values with a computed value (mean of column A here)\\n\",'-'*60, sep='')\n",
    "print(df.fillna(value=df['A'].mean()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GroupBy method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>Person</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GOOG</td>\n",
       "      <td>Sam</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GOOG</td>\n",
       "      <td>Charlie</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>Amy</td>\n",
       "      <td>340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>Vanessa</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FB</td>\n",
       "      <td>Carl</td>\n",
       "      <td>243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FB</td>\n",
       "      <td>Sarah</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   Person  Sales\n",
       "0    GOOG      Sam    200\n",
       "1    GOOG  Charlie    120\n",
       "2    MSFT      Amy    340\n",
       "3    MSFT  Vanessa    124\n",
       "4      FB     Carl    243\n",
       "5      FB    Sarah    350"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create dataframe\n",
    "data = {'Company':['GOOG','GOOG','MSFT','MSFT','FB','FB'],\n",
    "       'Person':['Sam','Charlie','Amy','Vanessa','Carl','Sarah'],\n",
    "       'Sales':[200,120,340,124,243,350]}\n",
    "df = pd.DataFrame(data)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Grouping by 'Company' column and listing mean sales\n",
      "-------------------------------------------------------\n",
      "         Sales\n",
      "Company       \n",
      "FB       296.5\n",
      "GOOG     160.0\n",
      "MSFT     232.0\n",
      "\n",
      "Grouping by 'Company' column and listing sum of sales\n",
      "-------------------------------------------------------\n",
      "         Sales\n",
      "Company       \n",
      "FB         593\n",
      "GOOG       320\n",
      "MSFT       464\n",
      "\n",
      "All in one line of command (Stats for 'FB')\n",
      "-----------------------------------------------------------------\n",
      "   Sales                                                       \n",
      "   count   mean        std    min     25%    50%     75%    max\n",
      "FB   2.0  296.5  75.660426  243.0  269.75  296.5  323.25  350.0\n",
      "\n",
      "Same type of extraction with little different command\n",
      "--------------------------------------------------------------------\n",
      "        Sales                                                      \n",
      "        count   mean         std    min    25%    50%    75%    max\n",
      "Company                                                            \n",
      "GOOG      2.0  160.0   56.568542  120.0  140.0  160.0  180.0  200.0\n",
      "MSFT      2.0  232.0  152.735065  124.0  178.0  232.0  286.0  340.0\n"
     ]
    }
   ],
   "source": [
    "byComp = df.groupby('Company')\n",
    "print(\"\\nGrouping by 'Company' column and listing mean sales\\n\",'-'*55, sep='')\n",
    "print(byComp.mean())\n",
    "print(\"\\nGrouping by 'Company' column and listing sum of sales\\n\",'-'*55, sep='')\n",
    "print(byComp.sum())\n",
    "# Note dataframe conversion of the series and transpose\n",
    "print(\"\\nAll in one line of command (Stats for 'FB')\\n\",'-'*65, sep='')\n",
    "print(pd.DataFrame(df.groupby('Company').describe().loc['FB']).transpose())\n",
    "print(\"\\nSame type of extraction with little different command\\n\",'-'*68, sep='')\n",
    "print(df.groupby('Company').describe().loc[['GOOG', 'MSFT']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merging, Joining, Concatenating\n",
    "### Concatenation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The DataFrame number 1\n",
      "------------------------------\n",
      "    A   B   C   D\n",
      "0  A0  B0  C0  D0\n",
      "1  A1  B1  C1  D1\n",
      "2  A2  B2  C2  D2\n",
      "3  A3  B3  C3  D3\n",
      "\n",
      "The DataFrame number 2\n",
      "------------------------------\n",
      "    A   B   C   D\n",
      "4  A4  B4  C4  D4\n",
      "5  A5  B5  C5  D5\n",
      "6  A6  B6  C6  D6\n",
      "7  A7  B7  C7  D7\n",
      "\n",
      "The DataFrame number 3\n",
      "------------------------------\n",
      "      A    B    C    D\n",
      "8    A8   B8   C8   D8\n",
      "9    A9   B9   C9   D9\n",
      "10  A10  B10  C10  D10\n",
      "11  A11  B11  C11  D11\n"
     ]
    }
   ],
   "source": [
    "# Creating data frames\n",
    "df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],\n",
    "                        'B': ['B0', 'B1', 'B2', 'B3'],\n",
    "                        'C': ['C0', 'C1', 'C2', 'C3'],\n",
    "                        'D': ['D0', 'D1', 'D2', 'D3']},\n",
    "                        index=[0, 1, 2, 3])\n",
    "\n",
    "df2 = pd.DataFrame({'A': ['A4', 'A5', 'A6', 'A7'],\n",
    "                        'B': ['B4', 'B5', 'B6', 'B7'],\n",
    "                        'C': ['C4', 'C5', 'C6', 'C7'],\n",
    "                        'D': ['D4', 'D5', 'D6', 'D7']},\n",
    "                         index=[4, 5, 6, 7])\n",
    "\n",
    "df3 = pd.DataFrame({'A': ['A8', 'A9', 'A10', 'A11'],\n",
    "                        'B': ['B8', 'B9', 'B10', 'B11'],\n",
    "                        'C': ['C8', 'C9', 'C10', 'C11'],\n",
    "                        'D': ['D8', 'D9', 'D10', 'D11']},\n",
    "                        index=[8,9,10,11])\n",
    "\n",
    "print(\"\\nThe DataFrame number 1\\n\",'-'*30, sep='')\n",
    "print(df1)\n",
    "print(\"\\nThe DataFrame number 2\\n\",'-'*30, sep='')\n",
    "print(df2)\n",
    "print(\"\\nThe DataFrame number 3\\n\",'-'*30, sep='')\n",
    "print(df3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "After concatenation along row\n",
      "------------------------------\n",
      "      A    B    C    D\n",
      "0    A0   B0   C0   D0\n",
      "1    A1   B1   C1   D1\n",
      "2    A2   B2   C2   D2\n",
      "3    A3   B3   C3   D3\n",
      "4    A4   B4   C4   D4\n",
      "5    A5   B5   C5   D5\n",
      "6    A6   B6   C6   D6\n",
      "7    A7   B7   C7   D7\n",
      "8    A8   B8   C8   D8\n",
      "9    A9   B9   C9   D9\n",
      "10  A10  B10  C10  D10\n",
      "11  A11  B11  C11  D11\n",
      "\n",
      "After concatenation along column\n",
      "------------------------------------------------------------\n",
      "      A    B    C    D    A    B    C    D    A    B    C    D\n",
      "0    A0   B0   C0   D0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
      "1    A1   B1   C1   D1  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
      "2    A2   B2   C2   D2  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
      "3    A3   B3   C3   D3  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
      "4   NaN  NaN  NaN  NaN   A4   B4   C4   D4  NaN  NaN  NaN  NaN\n",
      "5   NaN  NaN  NaN  NaN   A5   B5   C5   D5  NaN  NaN  NaN  NaN\n",
      "6   NaN  NaN  NaN  NaN   A6   B6   C6   D6  NaN  NaN  NaN  NaN\n",
      "7   NaN  NaN  NaN  NaN   A7   B7   C7   D7  NaN  NaN  NaN  NaN\n",
      "8   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   A8   B8   C8   D8\n",
      "9   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   A9   B9   C9   D9\n",
      "10  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  A10  B10  C10  D10\n",
      "11  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  A11  B11  C11  D11\n",
      "\n",
      "After filling missing values with zero\n",
      "------------------------------------------------------------\n",
      "     A   B   C   D   A   B   C   D    A    B    C    D\n",
      "0   A0  B0  C0  D0   0   0   0   0    0    0    0    0\n",
      "1   A1  B1  C1  D1   0   0   0   0    0    0    0    0\n",
      "2   A2  B2  C2  D2   0   0   0   0    0    0    0    0\n",
      "3   A3  B3  C3  D3   0   0   0   0    0    0    0    0\n",
      "4    0   0   0   0  A4  B4  C4  D4    0    0    0    0\n",
      "5    0   0   0   0  A5  B5  C5  D5    0    0    0    0\n",
      "6    0   0   0   0  A6  B6  C6  D6    0    0    0    0\n",
      "7    0   0   0   0  A7  B7  C7  D7    0    0    0    0\n",
      "8    0   0   0   0   0   0   0   0   A8   B8   C8   D8\n",
      "9    0   0   0   0   0   0   0   0   A9   B9   C9   D9\n",
      "10   0   0   0   0   0   0   0   0  A10  B10  C10  D10\n",
      "11   0   0   0   0   0   0   0   0  A11  B11  C11  D11\n"
     ]
    }
   ],
   "source": [
    "df_cat1 = pd.concat([df1,df2,df3], axis=0)\n",
    "print(\"\\nAfter concatenation along row\\n\",'-'*30, sep='')\n",
    "print(df_cat1)\n",
    "\n",
    "df_cat2 = pd.concat([df1,df2,df3], axis=1)\n",
    "print(\"\\nAfter concatenation along column\\n\",'-'*60, sep='')\n",
    "print(df_cat2)\n",
    "df_cat2.fillna(value=0, inplace=True)\n",
    "print(\"\\nAfter filling missing values with zero\\n\",'-'*60, sep='')\n",
    "print(df_cat2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merging by a common 'key'\n",
    "The **merge** function allows you to merge DataFrames together using a similar logic as merging SQL Tables together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The DataFrame 'left'\n",
      "------------------------------\n",
      "    A   B key\n",
      "0  A0  B0  K0\n",
      "1  A1  B1  K1\n",
      "2  A2  B2  K2\n",
      "3  A3  B3  K3\n",
      "\n",
      "The DataFrame 'right'\n",
      "------------------------------\n",
      "    C   D key\n",
      "0  C0  D0  K0\n",
      "1  C1  D1  K1\n",
      "2  C2  D2  K2\n",
      "3  C3  D3  K3\n"
     ]
    }
   ],
   "source": [
    "left = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'],\n",
    "                     'A': ['A0', 'A1', 'A2', 'A3'],\n",
    "                     'B': ['B0', 'B1', 'B2', 'B3']})\n",
    "   \n",
    "right = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'],\n",
    "                          'C': ['C0', 'C1', 'C2', 'C3'],\n",
    "                          'D': ['D0', 'D1', 'D2', 'D3']})\n",
    "\n",
    "print(\"\\nThe DataFrame 'left'\\n\",'-'*30, sep='')\n",
    "print(left)\n",
    "print(\"\\nThe DataFrame 'right'\\n\",'-'*30, sep='')\n",
    "print(right)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "After simple merging with 'inner' method\n",
      "--------------------------------------------------\n",
      "    A   B key   C   D\n",
      "0  A0  B0  K0  C0  D0\n",
      "1  A1  B1  K1  C1  D1\n",
      "2  A2  B2  K2  C2  D2\n",
      "3  A3  B3  K3  C3  D3\n"
     ]
    }
   ],
   "source": [
    "merge1= pd.merge(left,right,how='inner',on='key')\n",
    "print(\"\\nAfter simple merging with 'inner' method\\n\",'-'*50, sep='')\n",
    "print(merge1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merging on a set of keys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "left = pd.DataFrame({'key1': ['K0', 'K0', 'K1', 'K2'],\n",
    "                     'key2': ['K0', 'K1', 'K0', 'K1'],\n",
    "                        'A': ['A0', 'A1', 'A2', 'A3'],\n",
    "                        'B': ['B0', 'B1', 'B2', 'B3']})\n",
    "    \n",
    "right = pd.DataFrame({'key1': ['K0', 'K1', 'K1', 'K2'],\n",
    "                               'key2': ['K0', 'K0', 'K0', 'K0'],\n",
    "                                  'C': ['C0', 'C1', 'C2', 'C3'],\n",
    "                                  'D': ['D0', 'D1', 'D2', 'D3']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>key1</th>\n",
       "      <th>key2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A0</td>\n",
       "      <td>B0</td>\n",
       "      <td>K0</td>\n",
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       "      <th>1</th>\n",
       "      <td>A1</td>\n",
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       "      <td>K1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
       "      <td>K1</td>\n",
       "      <td>K0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A3</td>\n",
       "      <td>B3</td>\n",
       "      <td>K2</td>\n",
       "      <td>K1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A   B key1 key2\n",
       "0  A0  B0   K0   K0\n",
       "1  A1  B1   K0   K1\n",
       "2  A2  B2   K1   K0\n",
       "3  A3  B3   K2   K1"
      ]
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     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
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       "    .dataframe thead th {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>key1</th>\n",
       "      <th>key2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C0</td>\n",
       "      <td>D0</td>\n",
       "      <td>K0</td>\n",
       "      <td>K0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C1</td>\n",
       "      <td>D1</td>\n",
       "      <td>K1</td>\n",
       "      <td>K0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C2</td>\n",
       "      <td>D2</td>\n",
       "      <td>K1</td>\n",
       "      <td>K0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C3</td>\n",
       "      <td>D3</td>\n",
       "      <td>K2</td>\n",
       "      <td>K0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    C   D key1 key2\n",
       "0  C0  D0   K0   K0\n",
       "1  C1  D1   K1   K0\n",
       "2  C2  D2   K1   K0\n",
       "3  C3  D3   K2   K0"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>key1</th>\n",
       "      <th>key2</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A0</td>\n",
       "      <td>B0</td>\n",
       "      <td>K0</td>\n",
       "      <td>K0</td>\n",
       "      <td>C0</td>\n",
       "      <td>D0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
       "      <td>K1</td>\n",
       "      <td>K0</td>\n",
       "      <td>C1</td>\n",
       "      <td>D1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
       "      <td>K1</td>\n",
       "      <td>K0</td>\n",
       "      <td>C2</td>\n",
       "      <td>D2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A   B key1 key2   C   D\n",
       "0  A0  B0   K0   K0  C0  D0\n",
       "1  A2  B2   K1   K0  C1  D1\n",
       "2  A2  B2   K1   K0  C2  D2"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left, right, on=['key1', 'key2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
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       "     A    B key1 key2    C    D\n",
       "0   A0   B0   K0   K0   C0   D0\n",
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       "4   A3   B3   K2   K1  NaN  NaN\n",
       "5  NaN  NaN   K2   K0   C3   D3"
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     "execution_count": 192,
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    "pd.merge(left, right, how='outer',on=['key1', 'key2'])"
   ]
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   "execution_count": 195,
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    {
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       "    A   B key1 key2    C    D\n",
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       "3  A2  B2   K1   K0   C2   D2\n",
       "4  A3  B3   K2   K1  NaN  NaN"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
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    "pd.merge(left, right, how='left',on=['key1', 'key2'])"
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   "execution_count": 196,
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    {
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       "     A    B key1 key2   C   D\n",
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       "2   A2   B2   K1   K0  C2  D2\n",
       "3  NaN  NaN   K2   K0  C3  D3"
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    "pd.merge(left, right, how='right',on=['key1', 'key2'])"
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Joining\n",
    "Joining is a convenient method for combining the columns of two potentially differently-indexed DataFrames into a single DataFrame based on **'index keys'**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
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    "left = pd.DataFrame({'A': ['A0', 'A1', 'A2'],\n",
    "                     'B': ['B0', 'B1', 'B2']},\n",
    "                      index=['K0', 'K1', 'K2']) \n",
    "\n",
    "right = pd.DataFrame({'C': ['C0', 'C2', 'C3'],\n",
    "                    'D': ['D0', 'D2', 'D3']},\n",
    "                      index=['K0', 'K2', 'K3'])"
   ]
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   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
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    {
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   "metadata": {
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    {
     "data": {
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       "      A    B    C    D\n",
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       "K3  NaN  NaN   C3   D3"
      ]
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    "left.join(right, how='outer')"
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Useful operations\n",
    "### head() and unique values\n",
    "* head()\n",
    "* unique()\n",
    "* nunique()\n",
    "* value_count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {
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       "      <td>4</td>\n",
       "      <td>444</td>\n",
       "      <td>dd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>333</td>\n",
       "      <td>eeee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>222</td>\n",
       "      <td>fff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>666</td>\n",
       "      <td>gg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>777</td>\n",
       "      <td>h</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>666</td>\n",
       "      <td>iii</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>555</td>\n",
       "      <td>j</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col1  col2  col3\n",
       "0     1   444   aaa\n",
       "1     2   555    bb\n",
       "2     3   666     c\n",
       "3     4   444    dd\n",
       "4     5   333  eeee\n",
       "5     6   222   fff\n",
       "6     7   666    gg\n",
       "7     8   777     h\n",
       "8     9   666   iii\n",
       "9    10   555     j"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame({'col1':[1,2,3,4,5,6,7,8,9,10],\n",
    "                   'col2':[444,555,666,444,333,222,666,777,666,555],\n",
    "                   'col3':'aaa bb c dd eeee fff gg h iii j'.split()})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Method head() is for showing first few entries\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col1</th>\n",
       "      <th>col2</th>\n",
       "      <th>col3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>444</td>\n",
       "      <td>aaa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>555</td>\n",
       "      <td>bb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>666</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>444</td>\n",
       "      <td>dd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>333</td>\n",
       "      <td>eeee</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col1  col2  col3\n",
       "0     1   444   aaa\n",
       "1     2   555    bb\n",
       "2     3   666     c\n",
       "3     4   444    dd\n",
       "4     5   333  eeee"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"\\nMethod head() is for showing first few entries\\n\",'-'*50, sep='')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Finding unique values in 'col2'\n",
      "----------------------------------------\n",
      "[444 555 666 333 222 777]\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nFinding unique values in 'col2'\\n\",'-'*40, sep='') # Note 'unique' method applies to pd.series only\n",
    "print(df['col2'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Finding number of unique values in 'col2'\n",
      "---------------------------------------------\n",
      "6\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nFinding number of unique values in 'col2'\\n\",'-'*45, sep='')\n",
    "print(df['col2'].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Table of unique values in 'col2'\n",
      "----------------------------------------\n",
      "666    3\n",
      "444    2\n",
      "555    2\n",
      "222    1\n",
      "333    1\n",
      "777    1\n",
      "Name: col2, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nTable of unique values in 'col2'\\n\",'-'*40, sep='')\n",
    "t1=df['col2'].value_counts()\n",
    "print(t1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Applying functions\n",
    "Pandas work with **'apply'** method to accept any user-defined function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# Define a function\n",
    "def testfunc(x):\n",
    "    if (x> 500):\n",
    "        return (10*np.log10(x))\n",
    "    else:\n",
    "        return (x/10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   col1  col2  col3  FuncApplied\n",
      "0     1   444   aaa    44.400000\n",
      "1     2   555    bb    27.442930\n",
      "2     3   666     c    28.234742\n",
      "3     4   444    dd    44.400000\n",
      "4     5   333  eeee    33.300000\n",
      "5     6   222   fff    22.200000\n",
      "6     7   666    gg    28.234742\n",
      "7     8   777     h    28.904210\n",
      "8     9   666   iii    28.234742\n",
      "9    10   555     j    27.442930\n"
     ]
    }
   ],
   "source": [
    "df['FuncApplied'] = df['col2'].apply(testfunc)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Apply works with built-in function too!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   col1  col2  col3  FuncApplied  col3length\n",
      "0     1   444   aaa    44.400000           3\n",
      "1     2   555    bb    27.442930           2\n",
      "2     3   666     c    28.234742           1\n",
      "3     4   444    dd    44.400000           2\n",
      "4     5   333  eeee    33.300000           4\n",
      "5     6   222   fff    22.200000           3\n",
      "6     7   666    gg    28.234742           2\n",
      "7     8   777     h    28.904210           1\n",
      "8     9   666   iii    28.234742           3\n",
      "9    10   555     j    27.442930           1\n"
     ]
    }
   ],
   "source": [
    "df['col3length']= df['col3'].apply(len)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Combine 'apply' with lambda expession for in-line calculations**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    6.663332\n",
       "1    5.238600\n",
       "2    5.313637\n",
       "3    6.663332\n",
       "4    5.770615\n",
       "5    4.711688\n",
       "6    5.313637\n",
       "7    5.376264\n",
       "8    5.313637\n",
       "9    5.238600\n",
       "Name: FuncApplied, dtype: float64"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['FuncApplied'].apply(lambda x: np.sqrt(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Standard statistical functions directly apply to columns**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Sum of the column 'FuncApplied' is:  312.7942967255717\n",
      "Mean of the column 'FuncApplied' is:  31.27942967255717\n",
      "Std dev of the column 'FuncApplied' is:  7.4065059423607895\n",
      "Min and max of the column 'FuncApplied' are:  22.2 and 44.4\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nSum of the column 'FuncApplied' is: \",df['FuncApplied'].sum())\n",
    "print(\"Mean of the column 'FuncApplied' is: \",df['FuncApplied'].mean())\n",
    "print(\"Std dev of the column 'FuncApplied' is: \",df['FuncApplied'].std())\n",
    "print(\"Min and max of the column 'FuncApplied' are: \",df['FuncApplied'].min(),\"and\",df['FuncApplied'].max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deletion, sorting, list of column and row names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Getting the names of the columns**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Name of columns\n",
      "--------------------\n",
      "Index(['col1', 'col2', 'col3', 'FuncApplied', 'col3length'], dtype='object')\n",
      "\n",
      "Column names in a list of strings for later manipulation: ['col1', 'col2', 'col3', 'FuncApplied', 'col3length']\n",
      "\n",
      "Deleting last column by 'del' command (this affects the dataframe immediately, unlike drop method\n",
      "----------------------------------------------------------------------------------------------------\n",
      "   col1  col2  col3  FuncApplied\n",
      "0     1   444   aaa    44.400000\n",
      "1     2   555    bb    27.442930\n",
      "2     3   666     c    28.234742\n",
      "3     4   444    dd    44.400000\n",
      "4     5   333  eeee    33.300000\n",
      "5     6   222   fff    22.200000\n",
      "6     7   666    gg    28.234742\n",
      "7     8   777     h    28.904210\n",
      "8     9   666   iii    28.234742\n",
      "9    10   555     j    27.442930\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nName of columns\\n\",'-'*20, sep='')\n",
    "print(df.columns)\n",
    "l = list(df.columns)\n",
    "print(\"\\nColumn names in a list of strings for later manipulation:\",l)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Deletion by 'del' command** # This affects the dataframe immediately, unlike drop method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Deleting last column by 'del' command\n",
      "--------------------------------------------------\n",
      "   col1  col2  col3  FuncApplied\n",
      "0     1   444   aaa    44.400000\n",
      "1     2   555    bb    27.442930\n",
      "2     3   666     c    28.234742\n",
      "3     4   444    dd    44.400000\n",
      "4     5   333  eeee    33.300000\n",
      "5     6   222   fff    22.200000\n",
      "6     7   666    gg    28.234742\n",
      "7     8   777     h    28.904210\n",
      "8     9   666   iii    28.234742\n",
      "9    10   555     j    27.442930\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nDeleting last column by 'del' command\\n\",'-'*50, sep='')\n",
    "del df['col3length']\n",
    "print(df)\n",
    "df['col3length']= df['col3'].apply(len)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Sorting and Ordering a DataFrame **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col1</th>\n",
       "      <th>col2</th>\n",
       "      <th>col3</th>\n",
       "      <th>FuncApplied</th>\n",
       "      <th>col3length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>222</td>\n",
       "      <td>fff</td>\n",
       "      <td>22.200000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>333</td>\n",
       "      <td>eeee</td>\n",
       "      <td>33.300000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>444</td>\n",
       "      <td>aaa</td>\n",
       "      <td>44.400000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>444</td>\n",
       "      <td>dd</td>\n",
       "      <td>44.400000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>555</td>\n",
       "      <td>bb</td>\n",
       "      <td>27.442930</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>555</td>\n",
       "      <td>j</td>\n",
       "      <td>27.442930</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>666</td>\n",
       "      <td>c</td>\n",
       "      <td>28.234742</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>666</td>\n",
       "      <td>gg</td>\n",
       "      <td>28.234742</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>666</td>\n",
       "      <td>iii</td>\n",
       "      <td>28.234742</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>777</td>\n",
       "      <td>h</td>\n",
       "      <td>28.904210</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col1  col2  col3  FuncApplied  col3length\n",
       "5     6   222   fff    22.200000           3\n",
       "4     5   333  eeee    33.300000           4\n",
       "0     1   444   aaa    44.400000           3\n",
       "3     4   444    dd    44.400000           2\n",
       "1     2   555    bb    27.442930           2\n",
       "9    10   555     j    27.442930           1\n",
       "2     3   666     c    28.234742           1\n",
       "6     7   666    gg    28.234742           2\n",
       "8     9   666   iii    28.234742           3\n",
       "7     8   777     h    28.904210           1"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='col2') #inplace=False by default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col1</th>\n",
       "      <th>col2</th>\n",
       "      <th>col3</th>\n",
       "      <th>FuncApplied</th>\n",
       "      <th>col3length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>444</td>\n",
       "      <td>aaa</td>\n",
       "      <td>44.400000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>444</td>\n",
       "      <td>dd</td>\n",
       "      <td>44.400000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>333</td>\n",
       "      <td>eeee</td>\n",
       "      <td>33.300000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>777</td>\n",
       "      <td>h</td>\n",
       "      <td>28.904210</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>666</td>\n",
       "      <td>c</td>\n",
       "      <td>28.234742</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>666</td>\n",
       "      <td>gg</td>\n",
       "      <td>28.234742</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>666</td>\n",
       "      <td>iii</td>\n",
       "      <td>28.234742</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>555</td>\n",
       "      <td>bb</td>\n",
       "      <td>27.442930</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>555</td>\n",
       "      <td>j</td>\n",
       "      <td>27.442930</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>222</td>\n",
       "      <td>fff</td>\n",
       "      <td>22.200000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col1  col2  col3  FuncApplied  col3length\n",
       "0     1   444   aaa    44.400000           3\n",
       "3     4   444    dd    44.400000           2\n",
       "4     5   333  eeee    33.300000           4\n",
       "7     8   777     h    28.904210           1\n",
       "2     3   666     c    28.234742           1\n",
       "6     7   666    gg    28.234742           2\n",
       "8     9   666   iii    28.234742           3\n",
       "1     2   555    bb    27.442930           2\n",
       "9    10   555     j    27.442930           1\n",
       "5     6   222   fff    22.200000           3"
      ]
     },
     "execution_count": 292,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='FuncApplied',ascending=False) #inplace=False by default"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Find Null Values or Check for Null Values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col1</th>\n",
       "      <th>col2</th>\n",
       "      <th>col3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>abc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>555.0</td>\n",
       "      <td>def</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>666.0</td>\n",
       "      <td>ghi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>444.0</td>\n",
       "      <td>xyz</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col1   col2 col3\n",
       "0   1.0    NaN  abc\n",
       "1   2.0  555.0  def\n",
       "2   3.0  666.0  ghi\n",
       "3   NaN  444.0  xyz"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'col1':[1,2,3,np.nan],\n",
    "                   'col2':[np.nan,555,666,444],\n",
    "                   'col3':['abc','def','ghi','xyz']})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col1</th>\n",
       "      <th>col2</th>\n",
       "      <th>col3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    col1   col2   col3\n",
       "0  False   True  False\n",
       "1  False  False  False\n",
       "2  False  False  False\n",
       "3   True  False  False"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col1</th>\n",
       "      <th>col2</th>\n",
       "      <th>col3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>FILL</td>\n",
       "      <td>abc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>555</td>\n",
       "      <td>def</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>666</td>\n",
       "      <td>ghi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FILL</td>\n",
       "      <td>444</td>\n",
       "      <td>xyz</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col1  col2 col3\n",
       "0     1  FILL  abc\n",
       "1     2   555  def\n",
       "2     3   666  ghi\n",
       "3  FILL   444  xyz"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna('FILL')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Pivot Table**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>x</td>\n",
       "      <td>1</td>\n",
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       "      <th>1</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>y</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>x</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bar</td>\n",
       "      <td>two</td>\n",
       "      <td>y</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bar</td>\n",
       "      <td>one</td>\n",
       "      <td>x</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bar</td>\n",
       "      <td>one</td>\n",
       "      <td>y</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A    B  C  D\n",
       "0  foo  one  x  1\n",
       "1  foo  one  y  3\n",
       "2  foo  two  x  2\n",
       "3  bar  two  y  5\n",
       "4  bar  one  x  4\n",
       "5  bar  one  y  1"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {'A':['foo','foo','foo','bar','bar','bar'],\n",
    "     'B':['one','one','two','two','one','one'],\n",
    "       'C':['x','y','x','y','x','y'],\n",
    "       'D':[1,3,2,5,4,1]}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">bar</th>\n",
       "      <th>one</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">foo</th>\n",
       "      <th>one</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "C          x    y\n",
       "A   B            \n",
       "bar one  4.0  1.0\n",
       "    two  NaN  5.0\n",
       "foo one  1.0  3.0\n",
       "    two  2.0  NaN"
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Index out of 'A' and 'B', columns from 'C', actual numerical values from 'D'\n",
    "df.pivot_table(values='D',index=['A', 'B'],columns=['C'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>C</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">bar</th>\n",
       "      <th>one</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>FILLED</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">foo</th>\n",
       "      <th>one</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>2</td>\n",
       "      <td>FILLED</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "C             x       y\n",
       "A   B                  \n",
       "bar one       4       1\n",
       "    two  FILLED       5\n",
       "foo one       1       3\n",
       "    two       2  FILLED"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Index out of 'A' and 'B', columns from 'C', actual numerical values from 'D'\n",
    "df.pivot_table(values='D',index=['A', 'B'],columns=['C'], fill_value='FILLED')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas built-in Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Import packages**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Read in the CSV data file**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>1.339091</td>\n",
       "      <td>-0.163643</td>\n",
       "      <td>-0.646443</td>\n",
       "      <td>1.041233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02</th>\n",
       "      <td>-0.774984</td>\n",
       "      <td>0.137034</td>\n",
       "      <td>-0.882716</td>\n",
       "      <td>-2.253382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>-0.921037</td>\n",
       "      <td>-0.482943</td>\n",
       "      <td>-0.417100</td>\n",
       "      <td>0.478638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>-1.738808</td>\n",
       "      <td>-0.072973</td>\n",
       "      <td>0.056517</td>\n",
       "      <td>0.015085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>-0.905980</td>\n",
       "      <td>1.778576</td>\n",
       "      <td>0.381918</td>\n",
       "      <td>0.291436</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2000-01-01  1.339091 -0.163643 -0.646443  1.041233\n",
       "2000-01-02 -0.774984  0.137034 -0.882716 -2.253382\n",
       "2000-01-03 -0.921037 -0.482943 -0.417100  0.478638\n",
       "2000-01-04 -1.738808 -0.072973  0.056517  0.015085\n",
       "2000-01-05 -0.905980  1.778576  0.381918  0.291436"
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1=pd.read_csv('df1.csv', index_col=0)\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.039762</td>\n",
       "      <td>0.218517</td>\n",
       "      <td>0.103423</td>\n",
       "      <td>0.957904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.937288</td>\n",
       "      <td>0.041567</td>\n",
       "      <td>0.899125</td>\n",
       "      <td>0.977680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.780504</td>\n",
       "      <td>0.008948</td>\n",
       "      <td>0.557808</td>\n",
       "      <td>0.797510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.672717</td>\n",
       "      <td>0.247870</td>\n",
       "      <td>0.264071</td>\n",
       "      <td>0.444358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.053829</td>\n",
       "      <td>0.520124</td>\n",
       "      <td>0.552264</td>\n",
       "      <td>0.190008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          a         b         c         d\n",
       "0  0.039762  0.218517  0.103423  0.957904\n",
       "1  0.937288  0.041567  0.899125  0.977680\n",
       "2  0.780504  0.008948  0.557808  0.797510\n",
       "3  0.672717  0.247870  0.264071  0.444358\n",
       "4  0.053829  0.520124  0.552264  0.190008"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2=pd.read_csv('df2')\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Histogram of a single column**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2998d34be10>"
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2998d34b390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1['A'].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Histogram with a different set of arguments (list of columns, bins, figure size, etc)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000002998D985E48>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002998D9E6F60>]], dtype=object)"
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2998d824470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1.hist(column=['B','C'],bins=20,figsize=(10,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Histogram with generic plot method of Pandas**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2999061ac18>"
      ]
     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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s0b17d4+qqsrdnqup6dnmfHyQdc0P6wpEeC3IJWuZH9Z1z7Iq0V/5yldiw4YN8corr8Tx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\nJFKiAQAg0f8Hj3frIywNd9YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2998f659550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1.plot(kind='hist', bins=30, grid=True, figsize=(12,7))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Area plot**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2998db84780>"
      ]
     },
     "execution_count": 323,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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B7R3aZwghKE/J4HgGW6ttbK93xi50OMlEUYxO8zDLN6P7mMxhGZTWtVHdy2a4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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2998e163f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns #Plot style will change to Seaborn package style from now on\n",
    "df2.plot.area(alpha=0.4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Bar plot (with and without stacking)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29990bceba8>"
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2998f3aeda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29990f4bda0>"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GLeEJEybo0qVL39ru9XqVnPxNiKSkJHm93qDf8OrVujZGtEd19XXTEdrEaXklMoeD0/JK\nZA4Hp+WVnJe5rXlbKu12n5jlcrnk8/kCX/t8vialDAAAWtfui3UMGjRIFy9eVG1trRITE3X8+HEV\nFhZ2ZjZHivQbSAMAIkebS/jAgQOqq6uT2+3W0qVLVVhYKMuylJeXp969e9uREQCAqBRSCfft2zfw\nEaScnJzA9uzsbGVnZ9uTDACAKMfFOgAAMIQbOHSyGx9NtGcwBxwAIOpQwnAcu05+kzgBDkB4cTga\nAABDWAkDAKJWpL9FyEoYAABDWAkDaBYXngHsx0oYAABDKGEAAAyhhAEAMIQSBgDAEEoYAABDKGEA\nAAzhI0oAYAgfAwMrYQAADGElDP41DgCGUMKI+GurAkC04nA0AACGUMIAABhCCQMAYAglDACAIZQw\nAACGUMIAABhCCQMAYAglDACAIZQwAACGUMIAABjCZSvhOLZdZlPiUpsAwoqVMAAAhlDCAAAYQgkD\nAGAIJQwAgCGUMAAAhlDCAAAYQgkDAGAInxMGEBU25n/XttmbbZuMOx0rYQAADGElDISBXas0VmiA\ns7ESBgDAEEoYAABDKGEAAAwJ+p5wY2OjVq9erb///e+Kj4/XCy+8oP79+wf2l5WVac+ePerZs6ck\nac2aNbr33nvtSwwAQJQIWsIHDx6U3+/Xrl27VFlZqdLSUm3dujWwv6qqSuvWrVN6erqtQQEAiDZB\nS/jEiRMaNWqUJGnIkCGqqqpqsv/06dPavn27qqurlZWVpXnz5tmTFACAKBO0hL1er1wuV+Dr2NhY\nNTQ0KC7u9m+dPHmy8vPz5XK5VFxcrEOHDmn06NEtzuvRI1FxcbGdEL1jUlOTTUdoE6fllcgcDk7L\nK5E5HJyWV3Je5s7KG7SEXS6XfD5f4OvGxsZAAVuWpYKCAiUn3w6TmZmpM2fOtFrCV6/WdTRzp6iu\nvm46Qps4La9E5nBwWl6JzOHgtLyS8zK3NW9LpR307Ohhw4bpvffekyRVVlZq8ODBgX1er1dTpkyR\nz+eTZVmqqKjgvWEAAEIUdCU8btw4ffDBB3r88cdlWZZefPFFHThwQHV1dXK73Vq0aJHmzJmj+Ph4\njRgxQpmZmeHIDQCA4wUt4S5duuj5559vsm3QoEGBX+fm5io3N7fzkwEAEOW4WAcAAIZQwgAAGMJd\nlABEhRsfTbRveLZ9o3FnYyUMAIAhlDAAAIZQwgAAGEIJAwBgCCUMAIAhlDAAAIZE9EeUxpwrs3F6\nlo2zAQAIjpUwAACGUMIAABhCCQMAYAglDACAIRF9YhYARDPbrnfNta4dg5UwAACGUMIAABjC4WgA\nzeJQKWA/VsIAABgS0SvhjfnftW32ZtsmAwAQGlbCAAAYQgkDAGAIJQwAgCGUMAAAhlDCAAAYQgkD\nAGBIRH9ECQAQOWy7gIt0x17EhZUwAACGUMIAABhCCQMAYAglDACAIZQwAACGUMIAABhCCQMAYAgl\nDACAIZQwAACGUMIAABhCCQMAYAglDACAIZQwAACGcBclIAxsu/vMHXrnGSBaRHQJc9ssAEA0C3o4\nurGxUatWrZLb7ZbH49HFixeb7C8vL1deXp7cbrd2795tW1AAAKJN0BI+ePCg/H6/du3apcWLF6u0\ntDSwr76+XiUlJXr99de1Y8cO7dq1SzU1NbYGBgAgWgQt4RMnTmjUqFGSpCFDhqiqqiqw7/z580pL\nS1NKSori4+OVkZGhY8eO2ZcWAIAoEmNZltXaA1asWKHx48crMzNTkpSVlaWDBw8qLi5Ox48f1xtv\nvKGXXnpJkrRx40bdc889mj59uv3JAQBwuKArYZfLJZ/PF/i6sbFRcXFxze7z+XxKTk62ISYAANEn\naAkPGzZM7733niSpsrJSgwcPDuwbNGiQLl68qNraWvn9fh0/flxDhw61Ly0AAFEk6OHoxsZGrV69\nWmfPnpVlWXrxxRd15swZ1dXVye12q7y8XJs3b5ZlWcrLy9OsWbPClR0AAEcLWsIAAMAeXLYSAABD\nKGEAAAyhhAEAMMTxJdzY2Gg6wh3H7/ebjhCSr7/+2jFZ/+3LL780HaFNGhsb9c9//tNRfw6/+uor\nRfqpMF6v13SEDvP7/fr6669NxwiJydeDI0v4888/11NPPaWHH35YY8eOVVZWlp588klduHDBdLSo\nUl5ertGjR2vcuHF65513AtufeOIJg6ladu7cOT311FNatmyZjh49qkmTJmnSpEk6dOiQ6WgtunDh\nQpP/ioqKAr+OVMuXL5cknTx5UhMmTFBxcbGmTJmiyspKw8mat3fvXr388ss6ffq0Jk6cqJ///Oea\nOHGijh49ajpai0aOHKk9e/aYjtEmFy5c0Pz587V48WJVVlYqJydHkydPbvJ3RyT57LPPVFhYqNGj\nRys9PV0zZszQ4sWLVV1dHd4glgN5PB6rsrKyybaPP/7YcrvdhhJFp+nTp1u1tbXWV199ZXk8Hmvf\nvn2WZVnW7NmzDSdrXn5+vlVRUWHt27fPysjIsGpqaqzr169H9OsiMzPTmjBhguXxeKzZs2dbDzzw\ngDV79mzL4/GYjtaif2crKCiwLly4YFmWZV25csWaNWuWwVQtmzp1quXz+aw5c+ZYn376qWVZt/NO\nnTrVcLKWzZgxw1qzZo3l8XisiooK03FCMmvWLOuDDz6w/vSnP1kPPvigdeXKFcvn81kzZswwHa1Z\nc+fODbwePv74Y+s3v/mNderUKesXv/hFWHNE9K0MW+L3+/WTn/ykybYhQ4YYShM6j8ej+vr6Jtss\ny1JMTIx27txpKFXLunbtqpSUFEnSli1bVFBQoO9///uKiYkxnKx5jY2NevDBByVJFRUVuvvuuyUp\ncIW3SLR3714999xzmjlzpkaOHCmPx6MdO3aYjhWS2NhYDRgwQJLUu3fviD0k3bVrVyUmJiopKUn9\n+vWTdDtvpL6OJalbt25atWqVTp06pe3bt2vt2rUaPny4+vXrpzlz5piO16yGhgY99NBDsixLv/vd\n79S7d29Jkfvnz+v1auDAgZJu98f69eu1ePFiXbt2Law5IvOnE8R9992nZcuWadSoUUpOTpbP59OR\nI0d03333mY7WqmeeeUYrV67U5s2bFRsbazpOUH369FFJSYkWLFggl8ull19+WYWFhWF/kYZq4MCB\nWrFihdauXRu429f27dvVq1cvw8ladvfdd+ull17SunXrdOrUKdNxQuL1ejV16lTV1dVpz549+ulP\nf6rS0lLdc889pqM1Kzs7W0VFRRo8eLDmzZunUaNG6S9/+YuGDx9uOlqLrP9/j/LHP/6xNm3apOvX\nr+vYsWMR/TZFnz59tGjRIt26dUtJSUnasGGDXC6XUlNTTUdrVt++fbVq1So9/PDDOnz4sNLT03X4\n8GElJCSENYcjL9ZhWZYOHjyoEydOyOv1yuVyadiwYRo3blxE/+tWkl577TX1799f48aNMx0lqIaG\nBu3fv1+PPPJI4IVZU1Ojbdu2acWKFYbTfVtjY6PKy8s1duzYwLa3335b48ePD/sfrPbYt2+f9u3b\npzfeeMN0lKD8fr8++eQT3XXXXRowYID27t2radOmqWvXrqajNeujjz7S+++/r6tXr6p79+7KyMhQ\nVlaW6Vgt+sMf/qDHHnvMdIw2aWho0JEjRzRgwAAlJSWprKxMKSkpKigoUGJioul43+L3+7Vnzx6d\nO3dOP/jBD5SXl6dTp06pf//+6tGjR9hyOLKEAQCIBo48OxoAgGhACQMAYAglDACAIZQwAACGUMIA\nABjyf4qIcY/e6paCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29990cfb518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2.plot.bar(stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Lineplot**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x299911693c8>"
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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juKh8wgjERzcaGDM6MPfz7Q/mMGKYnqoMXcCAvI6aPGpQXr7xSUjLJ+y4jr0V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\nsD8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n6sAVqHnnZayoeVJewxxXb1ZRSYSeJxa2G+qzOb2uNJvy57seO4kvXfsSOp0WkihC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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2999215f898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1.plot.line(x=df1.index,y=['B','C'],figsize=(12,4),lw=1) # Note matplotlib arguments like 'lw' and 'figsize'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Scatterplot**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x299921d1748>"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hTdnMAK2Z0pYbz5BOE3S4WQHdgGAGQGN0HjTchAkrfMz4GYh3bs/Ra5eXO5+9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\nAAAAsEBgBgAAACz8D4WuqA5BY/deAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x299921c5ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1.plot.scatter(x='A',y='B',figsize=(12,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x299923aba20>"
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+4L9743/l9Y/+z8SXr/GRp+HWYsCdpRBHEYQ8eybtWnyo6/cJF95F2QwXD5FO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GWK5Y7iWLVYXmRdZNIa+szoa+/M4zz6+/8r5N3Be5Fs8sJCF8cMSeM6l9xZmtRQWBasY2ZkHGb6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\n+P8BnOBNeGKGiCMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x299923c6358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1.plot.scatter(x='A',y='B',c='C',cmap='coolwarm',figsize=(12,8)) # Color of the scatter dots set based on column C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x299934f4208>"
      ]
     },
     "execution_count": 344,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SiWzVIC8Vjhx5BF6vFybTFORyOerrm9HQ0Lzsa91uJz755APYbBaIRGLs2rUH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\nwHwgmwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29993524278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1.plot.scatter(x='A',y='B',s=10*np.exp(df1['C']),c='C',figsize=(12,8)) # Size of the dots set based on column C"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Boxplot**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x299939b5860>"
      ]
     },
     "execution_count": 346,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uXFj4qQBgCXDVB6pGLVxoJcLFVoD/8bcBVaFSF0BxsRWgkvxPKgBIRIQBIBERBoBERBgA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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29993b20828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2.plot.box()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Hexagonal bin plot for bivariate data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.017515</td>\n",
       "      <td>-0.420704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.576828</td>\n",
       "      <td>1.814651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.261503</td>\n",
       "      <td>-0.254697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.974428</td>\n",
       "      <td>0.030171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.605864</td>\n",
       "      <td>0.079012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B\n",
       "0  1.017515 -0.420704\n",
       "1  0.576828  1.814651\n",
       "2 -0.261503 -0.254697\n",
       "3  0.974428  0.030171\n",
       "4  0.605864  0.079012"
      ]
     },
     "execution_count": 348,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame(data=np.random.randn(1000,2),columns=['A','B'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29994d46eb8>"
      ]
     },
     "execution_count": 350,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xHLRcBqtLZWhZFWpGDd3/0jJtmB0buT2TaFbi9SwrJ9fQKNex5+AMMsVsYB1Q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mrVo1z2mJ21+5zkIJRStFyF50kyGR09YmlOoJjKuGjATsOOXPLRheKO+SqOGd+lypWAF18ocddZ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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29994d884a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.hexbin(x='A',y='B',gridsize=30,cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Kernel density estimation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29994f5b6d8>"
      ]
     },
     "execution_count": 352,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HnsyGQMh+teoHzDi6rxx2O+0pkQlTFLcp79jYWLz55pt37a+rq0NsbCyUSiVE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\nvY7684RR0mPoRVl3JQCAB57NfvepzAZQg3mhSZHeiArydMocv49JrUb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A0BRlE6SmSJkH\n79R59o1J07h6jivPGD8rkNTrJrgkUqEE981Yg/9Y/BusCs+AkMc9SFf21OCt3Pfwp6x3UaQucykj\nnhLrh5RYP+iEMpQpGdEWHoBwcAa7JI+4zicLYrzdkO8ar7I65uGKcNzK4eQLd66IgcCOutgTQe+l\n72CoqwUA8EQiBOx62O4xK0va0dHKpMAJBDyy101weZRiBXbEbcL/t/jfsCR4Afg87ndZ01eHd/Pe\nxxu33kZBZ7HLGPGH18RBLOQj1zOePaYqPg++gAnIa2/pR2ebxlnTm1YQ4+1mGC1GXLLSMafbZ8BC\nMT/82FAvpMQ5V2XMotGg8/DnbNvnvvsh8vcfpscoxjRTuHGRE56ZMz8MHl7OF54hEEaDj9QbjyTt\nxO8WvYRloYsgsFqJ1/U34H/yP8QfM/+K3I5Cp0en+6lkuH9JFOplgVCLPAEAooE+hKm4h4tSErg2\nKRDj7WbcaM2C1sykg3kKVago4oQUHlwV6/R8545DB0FptQAAoZ8fvO/dYPeYhdlN6O9l8malMiHm\nLYoYoQeB4Hr4yrzxUMJ2/Mfil7AibCmEfCH7XoOmGe8VfIQ/Zv4NeU6OTr93QQQCfRXI8eJW30Gt\nuezrssI2mE1TuzTqVIAYbzeCoimca+CCwMxtkcBgfumiWYGIDXVu5LWuvAx9l79j2wG7H7Fbv1w/\nYELW1Tq2nbYkChKpc1PgCAR78Jaq8GD8FrwyuCcu4nPf50ZNM/5R8BFez/yr04y4SMjHo+viUegR\nA9Ogl0BZlwulgnltNJhRXdYx6fOabhDj7UbkdRShXdcJABBCDHUN444Wi/jYucI+1TJ7oUwmtH30\nAdtWzkuDMsW+IDUAyLxUC4OeSU/xVEkxKzVkhB4EwtTAS+KJHXGb8PslL2NNxAqIhzDiL5161SlG\nfFaUD+Ykh6NYycSW8ACEmblaAoXZzXfpSZgoiPF2E2iaxsnas2zb0h4BUIzb7f5FkU4vPtL97Tcw\ntbYCAPhSKfwfftTuMdXtGhTlcCU/F98TA4GAfKUJ7oWHWIltsffjlSGMeG1PI7sSL1aXTeq8dq2K\nQ57/LLbtV3YBfD7j6Wtr7kN7C1FccyTkTucmFHeVoUHDPO3yaQF0jYwIi6+nFOsXOHcP2NjSjK7j\nx9i27/adEHnbJ1lK0zQun65gBVnCorxJyU+CW2NtxFdHLL/Dnf5O3vt4K+c9NPRPzqrX20OC5WvT\nUStjUk8l5gEESbhI88Ksprt1JUwAxHi7ATRN40TtObZtbA8DzIwAxIOrYiF2oiALTdNo+/hfXLnP\nGdFQrVxl97hVpR1obmBqCvP5PCxd4/xgPAJhMvAQK7E9diNeWfIbbEpYY2PES7sr8MfMv+Kj4gPo\n0ncPM8rEsCYtDA3RaWw7oPIy+7qypB0DOqPD5zBdIcbbDajsqUZ1by3ToPkwNTP7UAnhKqQn2JeG\nZS99Vy5hoHzQncfnI/CxH4JnZ565yWixEWRJTguFj5/CrjEJhKmGp9gDe1J24HeL/w1LQxawxU9o\n0LjRmoX/uP4GjlWfhNHiOAPK5/Ow7uH16BpMG/MdaIFcxOhKWCw0EW1xIMR4uwHWq25zRwhgkkLA\n5+HR9QlOXY2aurvRceAztu29dj0k4fa78LOv10HbbwAAyOQipC+NsntMAmGqopJ44eHEnfj3Bb9E\nsm8Se9xMmfFt7Vn8/safkNdR5LCgtvBATxhSl7Jt/xYubawopxkU5fqV06YixHhPcSq6q1HaXcE0\naMDcEg0AuHdhBEKduBqlaRrtH3/IFh4R+fvDd/NWu8ft6dIh70YD2160MhoSqXCYHgTC9CBEGYSf\nzX0cz837KSI8wtjjXfpu/KPgX/h7/gdsNspEs+ihTTAImLTP6J4S8MAYbE2fATXljvmb0x1ivKcw\nNE3jaPUJtm3uDAFtkMNfJcWmJVHOmxiAvqtXoM3PY9uBP/wx+BLJMD1GhqZpXDxRDouFWUEEBHsg\nwck67QSCqxHvHYNfpz+DhxN3QCGSs8eL1KX4w40/4duas7BQEyuiIlHKIV2yAgDABwXf3gr2vdwb\nDS4j7+pOEOM9hclrLUbV4F43TfFgbooFADy6LsGpQWqm7m507N/HtlWr1kCekGj3uGUFrWiu7wEA\n8HjAinvjSZAagTAEfB4fS0MWYu+iX2NZ6CJ2P9xMW3Cs5iT+69ZbaOif2GjwGds3gRIwXrDErhzW\nYLe39LO/W8LE4TDjTVEU9u7di127dmHPnj2oq6uzef/YsWN44IEHsHv3buzdu5fsi4wRmqaxv+Br\ntm3pCANtlGN+YgBmR/s6dV7fd5f77XjA7nEHdEabILU588PhF0iqhhEIw6EUKfBQwnb8Ov0ZG1d6\no6YZ/3XrLXxddQImi2mYEUaP0MMTXstXAgAkFj1U2nr2vVyrrS7CxOAw433mzBkYjUYcOHAAL774\nIl5//XX2Pb1ej7/85S/46KOPsH//fmg0Gpw/f95RU3FL8joKUd3N/Dhoig9TcwxkEgF2D9bcdRaO\ncJcDwNWzVaySmoenBPOXRdk9JoEwXYj0DMev0n6ObbH3QzSomU7RFE7WncNrmX9FXd/EGFe/++4D\nBIzXb6b6Frv6rq/ugrqdVBubSBxmvLOyspCRkQEASElJQWFhIfueWCzG/v37IZMxRTPMZjMkE3CD\nny5YKAu+rj7Jts1tEYBJit2r4+Dt4bz/R2NHOzo++4RtT5S7vKGmC+VFbWw7Y308RGLnbQsQCFMR\nAV+ANREr8O8LfolYFVcyt03Xjjez3sGJ2rN2Vy0T+fjCczETeS439UOh5363ZPU9sTgsTFej0UCp\nVLJtgUAAs9kMoVAIPp8PPz9GDevjjz+GTqfD0qVL7zYUAMDbWw6h0P4btr//1He1nqi4gDZdOwCA\ntghgbolGWmIAtq1y3h4wbbGg4I33QekHq3uFBCPxp49DIB29LOtQn41Bb8KlU1zwy6yUEKQvirJ7\nvo7GHb5n1rjT9bjTtQBjvx5/eOA/I36F05WXsC//CPRmAyiawtHqkyjvq8QvFv4QAcrxqxUqH3kA\n2VcvARSNWZ2ZyAzfBACoKGnHvVuTofKR37WvO302jr4WhxlvpVIJ7WDpR4DZAxcKhTbtN954AzU1\nNXjrrbdGNDrd3Tq75+Tv74GOjn67x3EmOpMOB/KPsm1zcwxkAhkeWhWLzk7nuaU6vzqC/rJBMRaB\nAAGPP4GufhPQP7r9tLt9Nhe+LUNvN7N/LpEKkb4s0uU/Q3f4nlnjTtfjTtcC2Hc9qapURMyPxIdF\n+1HTx8QklXVW4Vcn/hMPxm/FgqDU8S0GRB7wmL8I/TeuwdOghkyvxoDUFzRF4/SxYqy8L2HCr8XV\nmMhrudtDgMPc5qmpqfjuO6b8Y25uLuLj423e37t3LwwGA959913WfU4YmW9rz7L1uimDDObWSOxe\nFefUwiMDlRXoOsYFz/lt2QbpjGi7x62rUtsoNGWsi4NcSbZXCISJwk/mi1+mPoWNM9aBz2PMgd5i\nwEclB/BB0acYMOvHNa7v5q3s3neiOpM9Xprfgr6eAfsnTnCc8V67di3EYjF2796N1157DS+//DKO\nHj2KAwcOoKioCIcOHUJ5eTl+8IMfYM+ePTh9+rSjpuI2tOs6cLHxKts21Sdg9owALJsT7LQ5WXQ6\ntPzzf3G7QogsPgHe926we1yD3oSL33JVkqIT/BGbFGD3uAQCwRYBX4D7ZqzBi2lPw1/GZapktefh\nj5l/HVdKmTgwEF7LmJgnn4FWyPWMUAtNA5fPV0/MxKc5DnOb8/l8vPLKKzbHYmK4mtKlpaWO+tNu\nCU3TOFRxFBaaEVew9KugNIXjRxsSnbfPPZgWZu5kfph8uRxBP3nSbu1yALh8uhJaDaPJLJWLsHx9\nHMnpJhAcSJRnBH4z/3l8UXEUV1tuAgA6BtR489bb2BG3CRmhi8f0G/TZuAV9Vy6DNpuR2JmJ7LD7\nAAC1ZR1Qd2rhS+oR2AURaZki5HUUokjNPPDQNGCqT8Qvd6fBy4lu5J7zZ9GfeZNtBz76A4h87M8x\nryxpt4kuX7E+HjK52O5xCQTC8EiFEjyStBOPz3wIkkG5UzNtwYHyL/F+4ScYMI/e5S3y9oZq1RoA\ngLe+DUo9E2TLA3DkSOEwPQmjgRjvKYDerMf+0i/ZtqU9HKuTkpGeFOi0OQ1UV9kUHfFasRIeCxba\nPW5fzwAunuDc5fGzAhHt5MpoBMJ0Iz1oHl6a/xxCldyWXE5HAV6/+VfU9zWOehyf++4HfzDjJKHz\nFnvcqNbh/PW6u3UjjAJivKcAX1acRL+ZiVykTWL46+fhgZUxI/RyHBaNBi3/8w5gYVz4kohI+O9+\n2P5xLRROf10Mo4EZ11MlxbK1zhWdIRCmK4Fyf/w67RlkhC5mj3Xqu/CnrHdwsfHqqPTKBR4e8Nmw\nEQCg0rfDe4AJQOWBh5sXa1DX6h7R5c6AGG8Xp66vEZeauSA1uikJT2+aB9EE5LyPB5qi0PLP/4W5\nqwsAs88d8rNnwBfZ79bOvFSD9mbmx8zn87Bm80xSMYxAcCIigQi7E7bhR7MegVTAbNGZaQsOln+J\n94v2jSoaXbV2HUR+jPcsruMmG9zqSQP/PJiLPq3j6o27M8R4uzBmyoy/Z+0DeMyX3dLngx8uWo0Q\nJwZ6dB75ArrCArYd9KMnIPK3361dVdaOnOucAtOCFTMQGOJp97gEAsF+0gLn4jfzn0e4MoQ9ltOe\nPxiN3jxsX75IDL8HdwMAPIzdCO6vZN9T6cx490gBzBZS22KsEOPtwvwr5yj6aTUAgLbwscBjNRbO\ndF4JzL5rV9H97Tds2/veDVCmzLN73P5ePQ5/ks22w2d4I2VBuN3jEgiEicNf7osX035PlWk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XnSTPhu3gZZXNy4/0ZrUy8KbjWiqrQD3/fHBId5YemaWPgHDf0USCAQCJNNgNwPjy7ZjO65vTj2\n7S1oKhlvJ58WIKApDsZ2PT4PvgBDSCfmLp2FRcYo3Mq2oK5VAz8vKTzlU3cRQoz3MBgsRhR0FOFm\nWw6K1WWgYWvRVBIvrI+8B4tDFkDkgEhy2myGrrQYfVevQpOTBdpkuuMcedJM+GzcDHlC4rj+hk5r\nRGVxO8oKW9HZprnj/YAQDyzImIGwKG+ilEYgEFwSb4UX9uxcjYY6Nc6dLIKui/FIikxSBNfPhKlF\nj7LAWnQFZEEZK8Py1EQk+yVCIJoSu8ZDQoz39xgwD6C0qxI57fko6CyGkbrTYPpKvbE2ciUWBc+f\ncKNt0WmhLSiANjcb2sICUAN3iu7zhEJ4LFqC6Ae2QqcYew6jTmtEXaUaNeUdqK/uumOVDQAhESqk\nLAxHRLQPMdoEAmFKEB7piz0/yUBpfgtuXKqGXstEnItM0v+/vbuPbbpcGzj+7dqVbe3Y+xuMjjHc\nOGNHx7YcRQI7Z2gMRxIeXMY2lRkkqDHExE2C+iRzAgF8O3+IYCREIfyjMhMVjPqEYFxijNkY4xHY\nGA42HJzDXthwbce6rvfzB88qla2daNv9yvX5q73v3u119e69q733a3+kdi8grncOHQu/o9HRSGNf\nI4azeubHziM3IYf5sZmkm2f5/VilP8sdX7wdY6NcHOrmp8HznOlv58IvXbiUa8Lb5sbnsCx9MQsT\nFhCm+/O/zzx87hyX3v7XhAUbwJg+h5j7lxC9+H4M0TMxJUVjn8JBEc7RMa5c/oXLP1/j4vl+jxOH\n3Eyv13FXbgp/LZpNYopsjwshtCcsTEdu/iyy81JoPflvmr+/iN36/yeEGjER7ohgxHBjl9Gpxmgb\nOEfbwI0zmBn1RubNzCArdi4ZMy3MiZ7FTOP0/Ft4RxbvIYeV/+n6ho5rnfw8dGnSYg2QakqhKDmf\nopR8kqIS/BqX9eSJWwq3ISEBc0ERMfcvYcYcy++6v85zfZz44Wd6/v2Lx4Fnv5WWHkN2XgpZC5KY\nEaHd/wEJIcQ4g0HPXwvT+cs9afx0pofOn/pJSDZRkvsorVfbOd3fxmXbfzzGOMYcHsUcIMYYTXr0\nbNLNs0g1JZMclUhyZBJR4bf/TZ4/wx1ZvN/73wNc+KVr0n5L9Gxy43MoSLmHWabUgG0bxxT/neGf\nzoHLhenuezDfswhjevptPb5jxMmXn5yasE+nu1GwM+YnMi8nkZmxwX0RCiGEvxgMehbcncaCu9Pc\nbTnx8/mv+f9k4PogZ/rP0j7YQcdgJwMjg7eMv+YY4lp/G6f72zzazeEmkqOSSIiIJz4ilriIWNJM\nKWTFzPV3SoAfi7fL5aKuro6zZ89iNBrZtm0bGRm/fqfu2LFj7N69G4PBQGlpKWvWrPFXKLcw/OZ/\nGilRyWTGWMiJm89f4rOJNpoDFsvNjEnJWF787z/lvnQ6iI6JcJ+iMy4hirQ5McyyxDInM16+6iWE\nuOPFRcSyZPa9LJl9LwD9wwN0XLvAhWtd/Dx0mUvWyxMe9wRgHbVhvWbj/LVOj/YHLX9nQ3K5v0P3\nX/E+evQoDoeDjz76iJaWFnbu3Mm7774LwOjoKDt27KC+vp7IyEgqKyspKSkhMTEwP7H5ZN5jtPa3\nYwqPIjMmI2AnCwmkcKOB0icKGOizE5sQJb+CJoQQPiRExpEQGcffUgsAcCkXPfY+uocuccn2H3rs\nffTYe+kd7mPU5ZzwPrqtlwMSq9+K9/Hjx1m6dCkA+fn5nDr16xZuR0cHFouFmJgbPy5fWFhIY2Mj\nK1as8Fc4HmYao7k3rTAgjxVMkVFGIi1StIUQ4naE6cJINSWTakqm6KZ2l3IxOHKNHnsfV68PMnB9\ngKsjg+h1YfxjztKAxOa34m21WjGbf91+1uv1OJ1ODAYDVquV6Ohfj+AzmUxYrbd+x/hmcXFRGAx/\n/BD+yX5qTqtCKR/JZfoKpXxCKRcIrXy0lEsKMeQw+UHE/s7Fb8XbbDZjs9nc110uFwaDYcI+m83m\nUcwnMjBg99o/FaH0O8AQWvlILtNXKOUTSrlAaOUjuUx+XxPx28mXCwoKaGhoAKClpYXs7Gx3X1ZW\nFl1dXQwODuJwOGhqamLRokX+CkUIIYQIKX775P3ggw/y3XffUVFRgVKK7du3c/jwYex2O+Xl5bz4\n4ousX78epRSlpaWk/IETaQghhBB3Er8V77CwMLZs2eLRlpWV5b5cUlJCSUmJvx5eCCGECFl+2zYX\nQgghhH9I8RZCCCE0Roq3EEIIoTFSvIUQQgiNkeIthBBCaIwUbyGEEEJjpHgLIYQQGqNTSqlgByGE\nEEKIqZNP3kIIIYTGSPEWQgghNEaKtxBCCKExUryFEEIIjZHiLYQQQmiMFG8hhBBCY/x2StBguX79\nOps2baK/vx+TycRrr71GfHy8x222bdtGc3MzJpMJgD179hAeHu5zXDBMJZ/9+/fzxRdfAFBcXMzG\njRtRSrFs2TLmzp0LQH5+PjU1NYEOHwCXy0VdXR1nz57FaDSybds2MjIy3P3Hjh1j9+7dGAwGSktL\nWbNmjc8xweIrriNHjnDgwAH0ej3Z2dnU1dURFhbG6tWrMZvNAKSnp7Njx45gpeDmK5f9+/dz6NAh\n9+vt1VdfZe7cudNyXsB7Pr29vVRXV7tv29raSk1NDZWVldNybsadPHmSN998k4MHD3q0a2nNjJss\nFy2tmXGT5RLQNaNCzPvvv6/efvttpZRSR44cUVu3br3lNhUVFaq/v/93jwsGX3FdvHhRrV69Wjmd\nTuVyuVR5eblqbW1VnZ2d6umnnw5GyLf4+uuv1ebNm5VSSp04cUI988wz7j6Hw6EeeOABNTg4qEZG\nRtQjjzyient7vY4JJm9xDQ8Pq+XLlyu73a6UUur5559XR48eVdevX1erVq0KSrze+HqOa2pq1I8/\n/vi7xgTTVGNrbm5Wa9euVU6nc9rOjVJK7d27V61cuVKVlZV5tGttzSg1eS5aWzNKTZ6LUoFdMyG3\nbX78+HGWLl0KwLJly/j+++89+l0uF11dXdTW1lJRUUF9ff2UxgWLr7hSU1PZt28fer0enU6H0+lk\nxowZnD59mitXrrB27Vo2bNjA+fPngxE+4JlDfn4+p06dcvd1dHRgsViIiYnBaDRSWFhIY2Oj1zHB\n5C0uo9HIhx9+SGRkJIB7Ltra2hgeHubJJ5+kqqqKlpaWoMT+W76e49OnT7N3714qKyt57733pjQm\nmKYSm1KKrVu3UldXh16vn7ZzA2CxWNi1a9ct7VpbMzB5LlpbMzB5LhDYNaPpbfNDhw5x4MABj7aE\nhASio6MBMJlMDA0NefTb7XYef/xx1q1bx9jYGFVVVeTl5WG1Wr2OC4TbySc8PJz4+HiUUrz++uvk\n5uaSmZlJX18fTz31FCtWrKCpqYlNmzbxySefBCyXm1mtVvf2F4Ber8fpdGIwGDyed7iRo9Vq9Tom\nmLzFFRYWRmJiIgAHDx7EbrezZMkS2tvbWb9+PWVlZXR2drJhwwa++uqraZ0LwMMPP8yjjz6K2Wxm\n48aNfPPNN9N2XsB3PnBju/muu+5i3rx5AEREREzLuQF46KGH6O7uvqVda2sGJs9Fa2sGJs8FArtm\ngv9M/AFlZWWUlZV5tG3cuBGbzQaAzWZj5syZHv2RkZFUVVW53+ndd999tLW1YTabvY4LhNvJB2Bk\nZISXX34Zk8nEK6+8AkBeXh56vR6AoqIienp6UEqh0+n8nMWtbn5u4cbux/gL97d9NpuN6Ohor2OC\nyVdcLpeLN954gwsXLrBr1y50Oh2ZmZlkZGS4L8fGxtLb20taWlowUnDzlotSiieeeMJdJIqLizlz\n5sy0nRfwPTcAn3/+OVVVVe7r03VuvNHamvFFS2vGm0CvmZDbNi8oKODbb78FoKGhgcLCQo/+zs5O\nKisrGRsbY3R0lObmZhYuXOhzXLD4ikspxbPPPktOTg5btmxxF+x33nnH/Sm+ra2NtLS0oBRuuJFD\nQ0MDAC0tLWRnZ7v7srKy6OrqYnBwEIfDQVNTE4sWLfI6Jph8xVVbW8vIyAh79uxxv0Gsr69n586d\nAFy5cgWr1UpSUlJgA5+At1ysVisrV67EZrOhlOKHH34gLy9v2s4L+J4bgFOnTlFQUOC+Pl3nxhut\nrRlftLRmvAn0mgm5E5MMDw+zefNment7CQ8P56233iIpKYkPPvgAi8XC8uXL2bdvH19++SXh4eGs\nWrWKysrKSccFm698XC4X1dXV5Ofnu8dUV1czb948Nm3ahN1uR6/XU1tbS1ZWVlByGD8Ktr29HaUU\n27dv58yZM9jtdsrLy91HziqlKC0t5bHHHptwTLDin2oueXl5lJaWUlRU5H6jVFVVRXFxMS+99BKX\nL19Gp9PxwgsveBSQYPE1L59++ikHDx7EaDSyePFinnvuuWk7L+A7n6tXr7Ju3To+++wz9xiHwzEt\n52Zcd3c31dXVfPzxxxw+fFiTa2bcRLlobc2Mm2xeArlmQq54CyGEEKEu5LbNhRBCiFAnxVsIIYTQ\nGCneQgghhMZI8RZCCCE0Roq3EEIIoTFSvIUQQgiNkeIthBBCaIwUbyGEEEJj/g89zef3a1ypPwAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29994f4bd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2.plot.density(lw=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
